Abstract: The goal of this work was to improve the computational tools for near-infrared light modeling for clinical applications in photodynamic therapy (PDT) analysis and eventual treatment planning. The tools to facilitate this modeling were developed in NIRFAST, which is a finite-element based open-source package for modeling near-infrared light transport in tissue for medical applications. It includes full-featured segmentation and mesh creation tools for quickly and easily creating high quality 3D finite-element meshes from medical images, as well as visualization capabilities. These tools were used to determine estimators of treatment response to PDT based on information from contrast CT scans of the pancreas in a retrospective analysis of data from the VERTPAC-01 trial, which investigated the safety and efficacy of verteporfin PDT in 15 patients with locally advanced pancreatic adenocarcinoma. Contrast CT information was used to determine venous and arterial blood content, which was then correlated with necrotic volume as determined from post-treatment CT scans, as well as used to estimate tissue absorption in the pancreas and nearby blood vessels. Light modeling was used to test for correlation between light dose map contours and measured necrotic volume. Both contrast-derived venous blood content and calculated light distribution contours yielded strong correlation with measured necrotic volume, having R2 values of 0.85 and 0.91, respectively. This indicates that contrast CT can provide valuable information for estimating treatment response to photodynamic therapy, and also indicates that light attenuation is the dominant factor in treatment response, as opposed to other factors such as drug distribution. These R2 values are much stronger than those obtained by correlating the logarithm of energy delivered vs. necrotic volume in the VERTPAC-01 trial. This study demonstrates the effectiveness of using computational tools in light modeling for clinical applications, including the development of advanced segmentation, mesh creation, visualization, and modeling algorithms to allow for clinically viable processing. This is the first study to show that contrast CT provides needed surrogate dosimetry information to predict treatment response in a manner, which uses standard-of-care clinical images, rather than invasive dosimetry methods.